Remote sensing of salt-affected soils

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Remote sensing of salt-affected soils

Mashimbye, Zama Eric

2013-03

ENGLISH ABSTRACT: Concrete evidence of dryland salinity was observed in the Berg River catchment in the Western
Cape Province of South Africa. Soil salinization is a global land degradation hazard that
negatively affects the productivity of soils. Timely and accurate detection of soil salinity is
crucial for soil salinity monitoring and mitigation. It would be restrictive in terms of costs to use
traditional wet chemistry methods to detect and monitor soil salinity in the entire Berg River
catchment. The goal of this study was to investigate less tedious, accurate and cost effective
techniques for better monitoring.
Firstly, hyperspectral remote sensing (HRS) techniques that can best predict electrical
conductivity (EC) in the soil using individual bands, a unique normalized difference soil salinity
index (NDSI), partial least squares regression (PLSR) and bagging PLSR were investigated.
Spectral reflectance of dry soil samples was measured using an analytical spectral device
FieldSpec spectrometer in a darkroom. Soil salinity predictive models were computed using a
training dataset (n = 63). An independent validation dataset (n = 32) was used to validate the
models. Also, field-based regression predictive models for EC, pH, soluble Ca, Mg, Na, Cl and
SO4 were developed using soil samples (n = 23) collected in the Sandspruit catchment. These
soil samples were not ground or sieved and the spectra were measured using the sun as a source
of energy to emulate field conditions. Secondly, the value of NIR spectroscopy for the prediction
of EC, pH, soluble Ca, Mg, Na, Cl, and SO4 was evaluated using 49 soil samples. Spectral
reflectance of dry soil samples was measured using the Bruker multipurpose analyser
spectrometer. “Leave one out” cross validation (LOOCV) was used to calibrate PLSR predictive
models for EC, pH, soluble Ca, Mg, Na, Cl, and SO4. The models were validated using R2, root
mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD) and the
ratio of prediction to interquartile distance (RPIQ). Thirdly, owing to the suitability of land
components to map soil properties, the value of digital elevation models (DEMs) to delineate
accurate land components was investigated. Land components extracted from the second version
of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two
versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM
(GEOEYE DEM) derived from GeoEye stereo-images were compared. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The land components
were visually inspected and quantitatively analysed using the slope gradient standard deviation
measure and the mean slope gradient local variance ratio for accuracy.
Fourthly, the spatial accuracy of hydrological parameters (streamlines and catchment
boundaries) delineated from the 5-m resolution SUDEM (L1 and L2), the 30-m ASTER GDEM2
and the 90-m SRTM was evaluated. Reference catchment boundary and streamlines were
generated from the 1.5-m GEOEYE DEM. Catchment boundaries and streamlines were extracted
from the DEMs using the Arc Hydro module for ArcGIS. Visual inspection, correctness index, a
new Euclidean distance index and figure of merit index were used to validate the results. Finally,
the value of terrain attributes to model soil salinity based on the EC of the soil and groundwater
was investigated. Soil salinity regression predictive models were developed using CurveExpert
software. In addition, stepwise multiple linear regression soil salinity predictive models based on
annual evapotranspiration, the aridity index and terrain attributes were developed using
Statgraphics software. The models were validated using R2, standard error and correlation
coefficients. The models were also independently validated using groundwater hydro-census data
covering the Sandspruit catchment. This study found that good predictions of soil salinity based on bagging PLSR using first
derivative reflectance (R2 = 0.85), PLSR using untransformed reflectance (R2 = 0.70), a unique
NDSI (R2 = 0.65) and the untransformed individual band at 2257 nm (R2 = 0.60) predictive
models were achieved. Furthermore, it was established that reliable predictions of EC, pH,
soluble Ca, Mg, Na, Cl and SO4 in the field are possible using first derivative reflectance. The R2
for EC, pH, soluble Ca, Mg, Na, Cl and SO4 predictive models are 0.85, 0.50, 0.65, 0.84, 0.79,
0.81 and 0.58 respectively. Regarding NIR spectroscopy, validation R2 for all the PLSR
predictive models ranged from 0.62 to 0.87. RPD values were greater than 1.5 for all the models
and RMSECV ranged from 0.22 to 0.51. This study affirmed that NIR spectroscopy has the
potential to be used as a quick, reliable and less expensive method for evaluating salt-affected
soils. As regards hydrological parameters, the study concluded that valuable hydrological
parameters can be derived from DEMs. A new Euclidean distance ratio was proved to be a
reliable tool to compare raster data sets. Regarding land components, it was concluded that
higher resolution DEMs are required for delineating meaningful land components. It seems probable that land components may improve salinity modelling using hydrological modelling
and that they can be integrated with other data sets to map soil salinity more accurately at
catchment level. In the case of terrain attributes, the study established that promising soil salinity
predictions could be made based on slope, elevation, evapotranspiration and terrain wetness
index (TWI). Stepwise multiple linear regressions soil salinity predictive model based on
elevation, evapotranspiration and TWI yielded slightly more accurate prediction of soil salinity.
Overall, the study showed that it is possible to enhance soil salinity monitoring using HRS, NIR
spectroscopy, land components, hydrological parameters and terrain attributes.